Overview

Dataset statistics

Number of variables21
Number of observations696
Missing cells747
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.3 KiB
Average record size in memory168.2 B

Variable types

Text8
Categorical5
DateTime2
Numeric5
Unsupported1

Alerts

N_VICTIMAS is highly imbalanced (87.6%)Imbalance
Altura has 567 (81.5%) missing valuesMissing
Cruce has 171 (24.6%) missing valuesMissing
Dirección Normalizada has 8 (1.1%) missing valuesMissing
ID has unique valuesUnique
HH is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-29 06:23:49.263892
Analysis finished2024-05-29 06:23:56.390874
Duration7.13 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct696
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-29T00:23:57.428914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters6264
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique696 ?
Unique (%)100.0%

Sample

1st row2016-0001
2nd row2016-0002
3rd row2016-0003
4th row2016-0004
5th row2016-0005
ValueCountFrequency (%)
2016-0001 1
 
0.1%
2016-0013 1
 
0.1%
2016-0015 1
 
0.1%
2016-0003 1
 
0.1%
2016-0004 1
 
0.1%
2016-0005 1
 
0.1%
2016-0008 1
 
0.1%
2016-0009 1
 
0.1%
2016-0010 1
 
0.1%
2016-0012 1
 
0.1%
Other values (686) 686
98.6%
2024-05-29T00:23:59.260398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5568
88.9%
Dash Punctuation 696
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2143
38.5%
2 1020
18.3%
1 941
16.9%
6 275
 
4.9%
7 265
 
4.8%
8 262
 
4.7%
9 215
 
3.9%
3 154
 
2.8%
5 149
 
2.7%
4 144
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

N_VICTIMAS
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
676 
2
 
19
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters696
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Length

2024-05-29T00:24:00.111673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-29T00:24:00.688509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 696
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%

FECHA
Date

Distinct598
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2016-01-01 00:00:00
Maximum2021-12-30 00:00:00
2024-05-29T00:24:01.318231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:24:02.044818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AAAA
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.1882
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:02.621269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6837537
Coefficient of variation (CV)0.00083428972
Kurtosis-1.1149559
Mean2018.1882
Median Absolute Deviation (MAD)1
Skewness0.28590787
Sum1404659
Variance2.8350265
MonotonicityIncreasing
2024-05-29T00:24:02.941619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 144
20.7%
2018 143
20.5%
2017 131
18.8%
2019 103
14.8%
2021 97
13.9%
2020 78
11.2%
ValueCountFrequency (%)
2016 144
20.7%
2017 131
18.8%
2018 143
20.5%
2019 103
14.8%
2020 78
11.2%
2021 97
13.9%
ValueCountFrequency (%)
2021 97
13.9%
2020 78
11.2%
2019 103
14.8%
2018 143
20.5%
2017 131
18.8%
2016 144
20.7%

MM
Real number (ℝ)

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6925287
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:03.246299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5713088
Coefficient of variation (CV)0.53362621
Kurtosis-1.2513063
Mean6.6925287
Median Absolute Deviation (MAD)3
Skewness-0.047244355
Sum4658
Variance12.754246
MonotonicityNot monotonic
2024-05-29T00:24:03.605834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 78
11.2%
11 67
9.6%
8 65
9.3%
1 62
8.9%
5 60
8.6%
6 58
8.3%
2 56
8.0%
3 51
7.3%
7 51
7.3%
10 51
7.3%
Other values (2) 97
13.9%
ValueCountFrequency (%)
1 62
8.9%
2 56
8.0%
3 51
7.3%
4 50
7.2%
5 60
8.6%
6 58
8.3%
7 51
7.3%
8 65
9.3%
9 47
6.8%
10 51
7.3%
ValueCountFrequency (%)
12 78
11.2%
11 67
9.6%
10 51
7.3%
9 47
6.8%
8 65
9.3%
7 51
7.3%
6 58
8.3%
5 60
8.6%
4 50
7.2%
3 51
7.3%

DD
Real number (ℝ)

Distinct31
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.936782
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:04.009917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6396458
Coefficient of variation (CV)0.54211986
Kurtosis-1.1496069
Mean15.936782
Median Absolute Deviation (MAD)7
Skewness-0.032362106
Sum11092
Variance74.64348
MonotonicityNot monotonic
2024-05-29T00:24:04.403404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 31
 
4.5%
17 30
 
4.3%
3 27
 
3.9%
11 27
 
3.9%
27 27
 
3.9%
12 26
 
3.7%
14 26
 
3.7%
28 25
 
3.6%
10 25
 
3.6%
9 25
 
3.6%
Other values (21) 427
61.4%
ValueCountFrequency (%)
1 18
2.6%
2 22
3.2%
3 27
3.9%
4 23
3.3%
5 18
2.6%
6 19
2.7%
7 23
3.3%
8 14
2.0%
9 25
3.6%
10 25
3.6%
ValueCountFrequency (%)
31 13
1.9%
30 16
2.3%
29 22
3.2%
28 25
3.6%
27 27
3.9%
26 21
3.0%
25 24
3.4%
24 20
2.9%
23 24
3.4%
22 23
3.3%

HORA
Date

Distinct78
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum1900-01-02 00:00:00
Maximum2024-05-29 23:15:00
2024-05-29T00:24:04.962093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:24:05.589430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

HH
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.6 KiB
Distinct683
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:06.392586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length85
Median length52
Mean length28.863506
Min length2

Characters and Unicode

Total characters20089
Distinct characters77
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique672 ?
Unique (%)96.6%

Sample

1st rowAV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZ
2nd rowAV GRAL PAZ Y AV DE LOS CORRALES
3rd rowAV ENTRE RIOS 2034
4th rowAV LARRAZABAL Y GRAL VILLEGAS CONRADO
5th rowAV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑA
ValueCountFrequency (%)
av 617
 
16.4%
y 526
 
14.0%
de 121
 
3.2%
gral 97
 
2.6%
paz 70
 
1.9%
juan 48
 
1.3%
la 42
 
1.1%
au 40
 
1.1%
del 34
 
0.9%
san 31
 
0.8%
Other values (803) 2140
56.8%
2024-05-29T00:24:07.754396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8070
40.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12827
63.9%
Space Separator 3086
 
15.4%
Lowercase Letter 2549
 
12.7%
Other Punctuation 951
 
4.7%
Decimal Number 662
 
3.3%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Dash Punctuation 3
 
< 0.1%
Control 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 383
15.0%
e 288
11.3%
r 249
9.8%
o 208
 
8.2%
v 179
 
7.0%
n 175
 
6.9%
i 159
 
6.2%
l 143
 
5.6%
y 133
 
5.2%
t 98
 
3.8%
Other values (20) 534
20.9%
Uppercase Letter
ValueCountFrequency (%)
A 2315
18.0%
E 1077
 
8.4%
R 1064
 
8.3%
O 945
 
7.4%
L 756
 
5.9%
I 739
 
5.8%
N 699
 
5.4%
V 658
 
5.1%
T 527
 
4.1%
D 520
 
4.1%
Other values (17) 3527
27.5%
Decimal Number
ValueCountFrequency (%)
0 109
16.5%
1 99
15.0%
2 89
13.4%
5 80
12.1%
3 61
9.2%
9 53
8.0%
4 51
7.7%
6 43
 
6.5%
7 43
 
6.5%
8 34
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 680
71.5%
, 268
 
28.2%
/ 2
 
0.2%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
3086
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15376
76.5%
Common 4713
 
23.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2315
 
15.1%
E 1077
 
7.0%
R 1064
 
6.9%
O 945
 
6.1%
L 756
 
4.9%
I 739
 
4.8%
N 699
 
4.5%
V 658
 
4.3%
T 527
 
3.4%
D 520
 
3.4%
Other values (47) 6076
39.5%
Common
ValueCountFrequency (%)
3086
65.5%
. 680
 
14.4%
, 268
 
5.7%
0 109
 
2.3%
1 99
 
2.1%
2 89
 
1.9%
5 80
 
1.7%
3 61
 
1.3%
9 53
 
1.1%
4 51
 
1.1%
Other values (10) 137
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20054
99.8%
None 35
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (60) 8035
40.1%
None
ValueCountFrequency (%)
Ñ 16
45.7%
ó 6
 
17.1%
ñ 4
 
11.4%
á 4
 
11.4%
é 2
 
5.7%
í 2
 
5.7%
° 1
 
2.9%

TIPO_DE_CALLE
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
AVENIDA
429 
CALLE
136 
AUTOPISTA
66 
GRAL PAZ
65 

Length

Max length9
Median length7
Mean length6.8922414
Min length5

Characters and Unicode

Total characters4797
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAVENIDA
2nd rowGRAL PAZ
3rd rowAVENIDA
4th rowAVENIDA
5th rowAVENIDA

Common Values

ValueCountFrequency (%)
AVENIDA 429
61.6%
CALLE 136
 
19.5%
AUTOPISTA 66
 
9.5%
GRAL PAZ 65
 
9.3%

Length

2024-05-29T00:24:08.100979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-29T00:24:08.318707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
avenida 429
56.4%
calle 136
 
17.9%
autopista 66
 
8.7%
gral 65
 
8.5%
paz 65
 
8.5%

Most occurring characters

ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
N 429
 
8.9%
D 429
 
8.9%
V 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4732
98.6%
Space Separator 65
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1256
26.5%
E 565
11.9%
I 495
 
10.5%
N 429
 
9.1%
D 429
 
9.1%
V 429
 
9.1%
L 337
 
7.1%
C 136
 
2.9%
T 132
 
2.8%
P 131
 
2.8%
Other values (6) 393
 
8.3%
Space Separator
ValueCountFrequency (%)
65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4732
98.6%
Common 65
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1256
26.5%
E 565
11.9%
I 495
 
10.5%
N 429
 
9.1%
D 429
 
9.1%
V 429
 
9.1%
L 337
 
7.1%
C 136
 
2.9%
T 132
 
2.8%
P 131
 
2.8%
Other values (6) 393
 
8.3%
Common
ValueCountFrequency (%)
65
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
N 429
 
8.9%
D 429
 
8.9%
V 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Calle
Text

Distinct279
Distinct (%)40.1%
Missing1
Missing (%)0.1%
Memory size5.6 KiB
2024-05-29T00:24:08.624728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length42
Median length32
Mean length16.054676
Min length4

Characters and Unicode

Total characters11158
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)24.6%

Sample

1st rowPIEDRA BUENA AV.
2nd rowPAZ, GRAL. AV.
3rd rowENTRE RIOS AV.
4th rowLARRAZABAL AV.
5th rowSAN JUAN AV.
ValueCountFrequency (%)
av 419
 
22.3%
gral 86
 
4.6%
de 72
 
3.8%
paz 58
 
3.1%
autopista 53
 
2.8%
juan 38
 
2.0%
del 24
 
1.3%
la 23
 
1.2%
moreno 23
 
1.2%
san 22
 
1.2%
Other values (375) 1063
56.5%
2024-05-29T00:24:09.770601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9013
80.8%
Space Separator 1186
 
10.6%
Other Punctuation 876
 
7.9%
Decimal Number 81
 
0.7%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1622
18.0%
E 798
 
8.9%
R 795
 
8.8%
O 704
 
7.8%
I 590
 
6.5%
V 527
 
5.8%
L 518
 
5.7%
N 503
 
5.6%
T 488
 
5.4%
S 382
 
4.2%
Other values (16) 2086
23.1%
Decimal Number
ValueCountFrequency (%)
2 22
27.2%
1 21
25.9%
5 13
16.0%
7 8
 
9.9%
9 7
 
8.6%
8 7
 
8.6%
4 3
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 586
66.9%
, 279
31.8%
? 11
 
1.3%
Space Separator
ValueCountFrequency (%)
1186
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9013
80.8%
Common 2145
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1622
18.0%
E 798
 
8.9%
R 795
 
8.8%
O 704
 
7.8%
I 590
 
6.5%
V 527
 
5.8%
L 518
 
5.7%
N 503
 
5.6%
T 488
 
5.4%
S 382
 
4.2%
Other values (16) 2086
23.1%
Common
ValueCountFrequency (%)
1186
55.3%
. 586
27.3%
, 279
 
13.0%
2 22
 
1.0%
1 21
 
1.0%
5 13
 
0.6%
? 11
 
0.5%
7 8
 
0.4%
9 7
 
0.3%
8 7
 
0.3%
Other values (2) 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Altura
Real number (ℝ)

MISSING 

Distinct126
Distinct (%)97.7%
Missing567
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean3336.6357
Minimum30
Maximum16080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:10.063712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile365
Q11359
median2551
Q34500
95-th percentile9388.4
Maximum16080
Range16050
Interquartile range (IQR)3141

Descriptive statistics

Standard deviation3060.6418
Coefficient of variation (CV)0.91728379
Kurtosis5.6986682
Mean3336.6357
Median Absolute Deviation (MAD)1433
Skewness2.1594365
Sum430426
Variance9367528.2
MonotonicityNot monotonic
2024-05-29T00:24:10.266406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 2
 
0.3%
4300 2
 
0.3%
901 2
 
0.3%
4650 1
 
0.1%
16080 1
 
0.1%
466 1
 
0.1%
2250 1
 
0.1%
5455 1
 
0.1%
6700 1
 
0.1%
2428 1
 
0.1%
Other values (116) 116
 
16.7%
(Missing) 567
81.5%
ValueCountFrequency (%)
30 1
0.1%
133 1
0.1%
150 1
0.1%
156 1
0.1%
300 1
0.1%
305 1
0.1%
365 2
0.3%
390 1
0.1%
466 1
0.1%
550 1
0.1%
ValueCountFrequency (%)
16080 1
0.1%
15200 1
0.1%
14800 1
0.1%
14723 1
0.1%
11200 1
0.1%
11050 1
0.1%
10900 1
0.1%
7121 1
0.1%
7013 1
0.1%
6950 1
0.1%

Cruce
Text

MISSING 

Distinct317
Distinct (%)60.4%
Missing171
Missing (%)24.6%
Memory size5.6 KiB
2024-05-29T00:24:10.640583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length42
Median length30
Mean length13.933333
Min length3

Characters and Unicode

Total characters7315
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)39.6%

Sample

1st rowFERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowDE LOS CORRALES AV.
3rd rowVILLEGAS, CONRADO, GRAL.
4th rowSAENZ PE?A, LUIS, PRES.
5th rowESCALADA AV.
ValueCountFrequency (%)
av 216
 
18.0%
de 41
 
3.4%
gral 28
 
2.3%
la 16
 
1.3%
dr 15
 
1.3%
paz 14
 
1.2%
juan 13
 
1.1%
del 11
 
0.9%
cnel 11
 
0.9%
luis 9
 
0.8%
Other values (431) 823
68.8%
2024-05-29T00:24:11.290748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6079
83.1%
Space Separator 672
 
9.2%
Other Punctuation 524
 
7.2%
Decimal Number 32
 
0.4%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1079
17.7%
E 581
 
9.6%
R 546
 
9.0%
O 471
 
7.7%
N 395
 
6.5%
L 374
 
6.2%
I 365
 
6.0%
V 315
 
5.2%
D 267
 
4.4%
S 233
 
3.8%
Other values (16) 1453
23.9%
Decimal Number
ValueCountFrequency (%)
2 11
34.4%
1 7
21.9%
9 6
18.8%
5 2
 
6.2%
7 2
 
6.2%
4 2
 
6.2%
3 1
 
3.1%
8 1
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 319
60.9%
, 196
37.4%
? 9
 
1.7%
Space Separator
ValueCountFrequency (%)
672
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6079
83.1%
Common 1236
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1079
17.7%
E 581
 
9.6%
R 546
 
9.0%
O 471
 
7.7%
N 395
 
6.5%
L 374
 
6.2%
I 365
 
6.0%
V 315
 
5.2%
D 267
 
4.4%
S 233
 
3.8%
Other values (16) 1453
23.9%
Common
ValueCountFrequency (%)
672
54.4%
. 319
25.8%
, 196
 
15.9%
2 11
 
0.9%
? 9
 
0.7%
1 7
 
0.6%
9 6
 
0.5%
) 4
 
0.3%
( 4
 
0.3%
5 2
 
0.2%
Other values (4) 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%
Distinct635
Distinct (%)92.3%
Missing8
Missing (%)1.1%
Memory size5.6 KiB
2024-05-29T00:24:11.648353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length75
Median length51
Mean length30.401163
Min length8

Characters and Unicode

Total characters20916
Distinct characters50
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)86.3%

Sample

1st rowPIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowPAZ, GRAL. AV. y DE LOS CORRALES AV.
3rd rowENTRE RIOS AV. 2034
4th rowLARRAZABAL AV. y VILLEGAS, CONRADO, GRAL.
5th rowSAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.
ValueCountFrequency (%)
av 642
 
17.0%
y 537
 
14.2%
de 118
 
3.1%
gral 114
 
3.0%
paz 72
 
1.9%
autopista 54
 
1.4%
juan 51
 
1.3%
la 38
 
1.0%
del 35
 
0.9%
san 31
 
0.8%
Other values (736) 2094
55.3%
2024-05-29T00:24:12.377582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15215
72.7%
Space Separator 3100
 
14.8%
Other Punctuation 1401
 
6.7%
Decimal Number 622
 
3.0%
Lowercase Letter 543
 
2.6%
Initial Punctuation 18
 
0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Control 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2719
17.9%
E 1393
 
9.2%
R 1355
 
8.9%
O 1172
 
7.7%
I 958
 
6.3%
N 908
 
6.0%
L 889
 
5.8%
V 854
 
5.6%
T 704
 
4.6%
S 616
 
4.0%
Other values (18) 3647
24.0%
Decimal Number
ValueCountFrequency (%)
1 102
16.4%
0 91
14.6%
2 85
13.7%
5 78
12.5%
3 58
9.3%
9 49
7.9%
4 47
7.6%
6 41
6.6%
7 38
 
6.1%
8 33
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 923
65.9%
, 475
33.9%
2
 
0.1%
& 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
y 534
98.3%
e 9
 
1.7%
Space Separator
ValueCountFrequency (%)
3100
100.0%
Initial Punctuation
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Control
ValueCountFrequency (%)
 2
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15758
75.3%
Common 5158
 
24.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2719
17.3%
E 1393
 
8.8%
R 1355
 
8.6%
O 1172
 
7.4%
I 958
 
6.1%
N 908
 
5.8%
L 889
 
5.6%
V 854
 
5.4%
T 704
 
4.5%
S 616
 
3.9%
Other values (20) 4190
26.6%
Common
ValueCountFrequency (%)
3100
60.1%
. 923
 
17.9%
, 475
 
9.2%
1 102
 
2.0%
0 91
 
1.8%
2 85
 
1.6%
5 78
 
1.5%
3 58
 
1.1%
9 49
 
0.9%
4 47
 
0.9%
Other values (10) 150
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20870
99.8%
None 26
 
0.1%
Punctuation 20
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3100
14.9%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.4%
L 889
 
4.3%
V 854
 
4.1%
Other values (34) 6599
31.6%
None
ValueCountFrequency (%)
à 22
84.6%
 2
 
7.7%
 1
 
3.8%
° 1
 
3.8%
Punctuation
ValueCountFrequency (%)
18
90.0%
2
 
10.0%

COMUNA
Real number (ℝ)

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4252874
Minimum0
Maximum15
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:12.636328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q311
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3870501
Coefficient of variation (CV)0.59082563
Kurtosis-1.1254728
Mean7.4252874
Median Absolute Deviation (MAD)4
Skewness0.098753764
Sum5168
Variance19.246209
MonotonicityNot monotonic
2024-05-29T00:24:12.822799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 90
12.9%
4 76
10.9%
9 73
10.5%
8 65
9.3%
7 60
8.6%
3 45
 
6.5%
15 44
 
6.3%
13 40
 
5.7%
12 37
 
5.3%
14 35
 
5.0%
Other values (6) 131
18.8%
ValueCountFrequency (%)
0 2
 
0.3%
1 90
12.9%
2 25
 
3.6%
3 45
6.5%
4 76
10.9%
5 22
 
3.2%
6 21
 
3.0%
7 60
8.6%
8 65
9.3%
9 73
10.5%
ValueCountFrequency (%)
15 44
6.3%
14 35
5.0%
13 40
5.7%
12 37
5.3%
11 32
4.6%
10 29
 
4.2%
9 73
10.5%
8 65
9.3%
7 60
8.6%
6 21
 
3.0%
Distinct606
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:13.132943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length39
Median length38
Mean length37.66523
Min length11

Characters and Unicode

Total characters26215
Distinct characters19
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)79.0%

Sample

1st rowPoint (98896.78238426 93532.43437792)
2nd rowPoint (95832.05571093 95505.41641999)
3rd rowPoint (106684.29090040 99706.57687843)
4th rowPoint (99840.65224780 94269.16534422)
5th rowPoint (106980.32827929 100752.16915795)
ValueCountFrequency (%)
point 696
33.3%
28
 
1.3%
101721.59002217 5
 
0.2%
93844.25656649 5
 
0.2%
96563.66494817 4
 
0.2%
108815.73881056 4
 
0.2%
99620.34936816 4
 
0.2%
110483.29286598 4
 
0.2%
95832.05571093 4
 
0.2%
95505.41641999 4
 
0.2%
Other values (1202) 1330
63.7%
2024-05-29T00:24:13.685130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2457
 
9.4%
0 2454
 
9.4%
9 2196
 
8.4%
8 1717
 
6.5%
4 1670
 
6.4%
7 1655
 
6.3%
5 1639
 
6.3%
2 1608
 
6.1%
3 1589
 
6.1%
6 1574
 
6.0%
Other values (9) 7656
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18559
70.8%
Lowercase Letter 2784
 
10.6%
Other Punctuation 1392
 
5.3%
Space Separator 1392
 
5.3%
Close Punctuation 696
 
2.7%
Open Punctuation 696
 
2.7%
Uppercase Letter 696
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2457
13.2%
0 2454
13.2%
9 2196
11.8%
8 1717
9.3%
4 1670
9.0%
7 1655
8.9%
5 1639
8.8%
2 1608
8.7%
3 1589
8.6%
6 1574
8.5%
Lowercase Letter
ValueCountFrequency (%)
o 696
25.0%
t 696
25.0%
n 696
25.0%
i 696
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1392
100.0%
Space Separator
ValueCountFrequency (%)
1392
100.0%
Close Punctuation
ValueCountFrequency (%)
) 696
100.0%
Open Punctuation
ValueCountFrequency (%)
( 696
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22735
86.7%
Latin 3480
 
13.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2457
10.8%
0 2454
10.8%
9 2196
9.7%
8 1717
7.6%
4 1670
 
7.3%
7 1655
 
7.3%
5 1639
 
7.2%
2 1608
 
7.1%
3 1589
 
7.0%
6 1574
 
6.9%
Other values (4) 4176
18.4%
Latin
ValueCountFrequency (%)
o 696
20.0%
t 696
20.0%
n 696
20.0%
i 696
20.0%
P 696
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2457
 
9.4%
0 2454
 
9.4%
9 2196
 
8.4%
8 1717
 
6.5%
4 1670
 
6.4%
7 1655
 
6.3%
5 1639
 
6.3%
2 1608
 
6.1%
3 1589
 
6.1%
6 1574
 
6.0%
Other values (9) 7656
29.2%

pos x
Text

Distinct605
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:13.990180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length12
Mean length11.827586
Min length1

Characters and Unicode

Total characters8232
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)78.4%

Sample

1st row-58.47533969
2nd row-58.50877521
3rd row-58.39040293
4th row-58.46503904
5th row-58.38718297
ValueCountFrequency (%)
12
 
1.7%
58.44451316 5
 
0.7%
58.50073810 4
 
0.6%
58.46743471 4
 
0.6%
58.50877521 4
 
0.6%
58.48727942 3
 
0.4%
58.49491054 3
 
0.4%
58.47976785 3
 
0.4%
58.38526125 3
 
0.4%
58.37533517 3
 
0.4%
Other values (595) 652
93.7%
2024-05-29T00:24:14.496615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6852
83.2%
Other Punctuation 696
 
8.5%
Dash Punctuation 684
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1213
17.7%
8 1126
16.4%
4 911
13.3%
3 571
8.3%
1 539
7.9%
2 517
7.5%
6 512
7.5%
9 509
7.4%
7 497
7.3%
0 457
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

pos y
Text

Distinct605
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-05-29T00:24:14.830080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length12
Mean length11.827586
Min length1

Characters and Unicode

Total characters8232
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)78.4%

Sample

1st row-34.68757022
2nd row-34.66977709
3rd row-34.63189362
4th row-34.68092974
5th row-34.62246630
ValueCountFrequency (%)
12
 
1.7%
34.68475866 5
 
0.7%
34.54979510 4
 
0.6%
34.53476874 4
 
0.6%
34.66977709 4
 
0.6%
34.63652467 3
 
0.4%
34.54795581 3
 
0.4%
34.69153196 3
 
0.4%
34.57805810 3
 
0.4%
34.59276462 3
 
0.4%
Other values (595) 652
93.7%
2024-05-29T00:24:15.310699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6852
83.2%
Other Punctuation 696
 
8.5%
Dash Punctuation 684
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1172
17.1%
3 1147
16.7%
6 929
13.6%
5 725
10.6%
7 531
7.7%
9 502
7.3%
2 479
7.0%
1 463
 
6.8%
8 458
 
6.7%
0 446
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%

PARTICIPANTES
Categorical

Distinct41
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
PEATON-PASAJEROS
105 
MOTO-AUTO
83 
MOTO-CARGAS
78 
PEATON-AUTO
77 
MOTO-PASAJEROS
46 
Other values (36)
307 

Length

Max length19
Median length18
Mean length12.248563
Min length5

Characters and Unicode

Total characters8525
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.9%

Sample

1st rowMOTO-AUTO
2nd rowAUTO-PASAJEROS
3rd rowMOTO-AUTO
4th rowMOTO-SD
5th rowMOTO-PASAJEROS

Common Values

ValueCountFrequency (%)
PEATON-PASAJEROS 105
15.1%
MOTO-AUTO 83
11.9%
MOTO-CARGAS 78
11.2%
PEATON-AUTO 77
11.1%
MOTO-PASAJEROS 46
 
6.6%
MOTO-OBJETO FIJO 40
 
5.7%
PEATON-CARGAS 38
 
5.5%
AUTO-AUTO 31
 
4.5%
PEATON-MOTO 30
 
4.3%
MOTO-MOTO 25
 
3.6%
Other values (31) 143
20.5%

Length

2024-05-29T00:24:15.545179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peaton-pasajeros 105
13.8%
moto-auto 83
10.9%
moto-cargas 78
10.3%
peaton-auto 77
10.1%
fijo 63
 
8.3%
moto-pasajeros 46
 
6.1%
moto-objeto 40
 
5.3%
peaton-cargas 38
 
5.0%
auto-auto 31
 
4.1%
peaton-moto 30
 
4.0%
Other values (32) 168
22.1%

Most occurring characters

ValueCountFrequency (%)
O 1612
18.9%
A 1241
14.6%
T 1008
11.8%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 367
 
4.3%
R 335
 
3.9%
J 304
 
3.6%
Other values (12) 1429
16.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7782
91.3%
Dash Punctuation 679
 
8.0%
Space Separator 63
 
0.7%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1612
20.7%
A 1241
15.9%
T 1008
13.0%
E 554
 
7.1%
S 541
 
7.0%
P 455
 
5.8%
M 367
 
4.7%
R 335
 
4.3%
J 304
 
3.9%
U 301
 
3.9%
Other values (9) 1064
13.7%
Dash Punctuation
ValueCountFrequency (%)
- 679
100.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7782
91.3%
Common 743
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1612
20.7%
A 1241
15.9%
T 1008
13.0%
E 554
 
7.1%
S 541
 
7.0%
P 455
 
5.8%
M 367
 
4.7%
R 335
 
4.3%
J 304
 
3.9%
U 301
 
3.9%
Other values (9) 1064
13.7%
Common
ValueCountFrequency (%)
- 679
91.4%
63
 
8.5%
_ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1612
18.9%
A 1241
14.6%
T 1008
11.8%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 367
 
4.3%
R 335
 
3.9%
J 304
 
3.6%
Other values (12) 1429
16.8%

VICTIMA
Categorical

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
MOTO
295 
PEATON
264 
AUTO
83 
BICICLETA
 
29
SD
 
9
Other values (5)
 
16

Length

Max length11
Median length4
Mean length5.0201149
Min length2

Characters and Unicode

Total characters3494
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowMOTO
2nd rowAUTO
3rd rowMOTO
4th rowMOTO
5th rowMOTO

Common Values

ValueCountFrequency (%)
MOTO 295
42.4%
PEATON 264
37.9%
AUTO 83
 
11.9%
BICICLETA 29
 
4.2%
SD 9
 
1.3%
CARGAS 7
 
1.0%
PASAJEROS 5
 
0.7%
MOVIL 2
 
0.3%
OBJETO FIJO 1
 
0.1%
PEATON_MOTO 1
 
0.1%

Length

2024-05-29T00:24:15.759288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-29T00:24:16.063869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
moto 295
42.3%
peaton 264
37.9%
auto 83
 
11.9%
bicicleta 29
 
4.2%
sd 9
 
1.3%
cargas 7
 
1.0%
pasajeros 5
 
0.7%
movil 2
 
0.3%
objeto 1
 
0.1%
fijo 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (11) 127
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3492
99.9%
Space Separator 1
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (9) 125
 
3.6%
Space Separator
ValueCountFrequency (%)
1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3492
99.9%
Common 2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (9) 125
 
3.6%
Common
ValueCountFrequency (%)
1
50.0%
_ 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (11) 127
 
3.6%

ACUSADO
Categorical

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
AUTO
204 
PASAJEROS
173 
CARGAS
146 
OBJETO FIJO
62 
MOTO
57 
Other values (5)
54 

Length

Max length11
Median length9
Mean length6.3678161
Min length2

Characters and Unicode

Total characters4432
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAUTO
2nd rowPASAJEROS
3rd rowAUTO
4th rowSD
5th rowPASAJEROS

Common Values

ValueCountFrequency (%)
AUTO 204
29.3%
PASAJEROS 173
24.9%
CARGAS 146
21.0%
OBJETO FIJO 62
 
8.9%
MOTO 57
 
8.2%
SD 23
 
3.3%
MULTIPLE 17
 
2.4%
BICICLETA 7
 
1.0%
OTRO 6
 
0.9%
TREN 1
 
0.1%

Length

2024-05-29T00:24:16.362163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-29T00:24:16.607338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
auto 204
26.9%
pasajeros 173
22.8%
cargas 146
19.3%
objeto 62
 
8.2%
fijo 62
 
8.2%
moto 57
 
7.5%
sd 23
 
3.0%
multiple 17
 
2.2%
bicicleta 7
 
0.9%
otro 6
 
0.8%

Most occurring characters

ValueCountFrequency (%)
A 849
19.2%
O 689
15.5%
S 515
11.6%
T 354
8.0%
R 326
 
7.4%
J 297
 
6.7%
E 260
 
5.9%
U 221
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (9) 571
12.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4370
98.6%
Space Separator 62
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 849
19.4%
O 689
15.8%
S 515
11.8%
T 354
8.1%
R 326
 
7.5%
J 297
 
6.8%
E 260
 
5.9%
U 221
 
5.1%
P 190
 
4.3%
C 160
 
3.7%
Other values (8) 509
11.6%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4370
98.6%
Common 62
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 849
19.4%
O 689
15.8%
S 515
11.8%
T 354
8.1%
R 326
 
7.5%
J 297
 
6.8%
E 260
 
5.9%
U 221
 
5.1%
P 190
 
4.3%
C 160
 
3.7%
Other values (8) 509
11.6%
Common
ValueCountFrequency (%)
62
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 849
19.2%
O 689
15.5%
S 515
11.6%
T 354
8.0%
R 326
 
7.4%
J 297
 
6.7%
E 260
 
5.9%
U 221
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (9) 571
12.9%

Interactions

2024-05-29T00:23:54.415643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:50.732885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:51.984440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:52.917277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.689666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:54.588429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:50.901588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:52.250837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.083121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.815832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:54.778180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:51.066843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:52.435120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.234791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.967978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:54.983144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:51.617113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:52.614526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.411927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:54.100862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:55.177730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:51.784781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:52.762317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:53.548115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-29T00:23:54.258031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-29T00:23:55.471588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-29T00:23:55.981169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-29T00:23:56.258003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDN_VICTIMASFECHAAAAAMMDDHORAHHLUGAR_DEL_HECHOTIPO_DE_CALLECalleAlturaCruceDirección NormalizadaCOMUNAXY (CABA)pos xpos yPARTICIPANTESVICTIMAACUSADO
02016-000112016-01-012016112024-05-294AV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZAVENIDAPIEDRA BUENA AV.NaNFERNANDEZ DE LA CRUZ, F., GRAL. AV.PIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.8Point (98896.78238426 93532.43437792)-58.47533969-34.68757022MOTO-AUTOMOTOAUTO
12016-000212016-01-022016122024-05-291AV GRAL PAZ Y AV DE LOS CORRALESGRAL PAZPAZ, GRAL. AV.NaNDE LOS CORRALES AV.PAZ, GRAL. AV. y DE LOS CORRALES AV.9Point (95832.05571093 95505.41641999)-58.50877521-34.66977709AUTO-PASAJEROSAUTOPASAJEROS
22016-000312016-01-032016132024-05-297AV ENTRE RIOS 2034AVENIDAENTRE RIOS AV.2034.0NaNENTRE RIOS AV. 20341Point (106684.29090040 99706.57687843)-58.39040293-34.63189362MOTO-AUTOMOTOAUTO
32016-000412016-01-1020161102024-05-290AV LARRAZABAL Y GRAL VILLEGAS CONRADOAVENIDALARRAZABAL AV.NaNVILLEGAS, CONRADO, GRAL.LARRAZABAL AV. y VILLEGAS, CONRADO, GRAL.8Point (99840.65224780 94269.16534422)-58.46503904-34.68092974MOTO-SDMOTOSD
42016-000512016-01-2120161212024-05-295AV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑAAVENIDASAN JUAN AV.NaNSAENZ PE?A, LUIS, PRES.SAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.1Point (106980.32827929 100752.16915795)-58.38718297-34.62246630MOTO-PASAJEROSMOTOPASAJEROS
52016-000812016-01-2420161242024-05-2918AV 27 DE FEBRERO Y AV ESCALADAAVENIDA27 DE FEBRERO AV.NaNESCALADA AV.27 DE FEBRERO AV. y ESCALADA AV.8Point (101721.59002217 93844.25656649)-58.44451316-34.68475866MOTO-OBJETO FIJOMOTOOBJETO FIJO
62016-000912016-01-2420161242024-05-2919NOGOYA Y JOAQUIN V. GONZALESCALLENOGOYANaNGONZALEZ, JOAQUIN V.NOGOYA y GONZALEZ, JOAQUIN V.11Point (96545.87592078 102330.67262199)-58.50095869-34.60825440MOTO-AUTOMOTOAUTO
72016-001012016-01-2920161292024-05-2915AV GENERAL PAZ Y AV DE LOS CORRALESGRAL PAZPAZ, GRAL. AV.NaNDE LOS CORRALES AV.PAZ, GRAL. AV. y DE LOS CORRALES AV.9Point (95832.05571093 95505.41641999)-58.50877521-34.66977709MOTO-AUTOMOTOAUTO
82016-001212016-02-082016282024-05-291AV BELGRANO Y BERNARDO DE IRIGOYENAVENIDABELGRANO AV.NaNIRIGOYEN, BERNARDO DEBELGRANO AV. e IRIGOYEN, BERNARDO DE1Point (107595.35084333 101797.50052813)-58.38048577-34.61303893MOTO-CARGASMOTOCARGAS
92016-001312016-02-1020162102024-05-2911AV ENTRE RIOS 1366AVENIDAENTRE RIOS AV.1366.0NaNENTRE RIOS AV. 13661Point (106616.41069662 100496.44662323)-58.39114932-34.62477387PEATON-AUTOPEATONAUTO
IDN_VICTIMASFECHAAAAAMMDDHORAHHLUGAR_DEL_HECHOTIPO_DE_CALLECalleAlturaCruceDirección NormalizadaCOMUNAXY (CABA)pos xpos yPARTICIPANTESVICTIMAACUSADO
6862021-008812021-12-0120211212024-05-2915AV. MOROE Y 3 DE FEBREROCALLEMONROENaN3 DE FEBREROMONROE y 3 DE FEBRERO13Point (100732.60222975 108177.68150062)-58.45531707-34.55555257MOTO-AUTOMOTOAUTO
6872021-008912021-12-0220211222024-05-291AV. GAONA 3655AVENIDAGAONA AV.3655.0NaNGAONA AV. 365511Point (98804.41713890 100872.30706871)-58.47633683-34.62140594MOTO-AUTOMOTOAUTO
6882021-009012021-12-10202112102024-05-2911AV. 9 DE JULIO Y LAVALLEAVENIDA9 DE JULIO AV.NaNLAVALLE9 DE JULIO AV. y LAVALLE1Point (107467.87595573 102960.02837514)-58.38188582-34.60256036PEATON-PASAJEROSPEATONPASAJEROS
6892021-009112021-12-11202112112024-05-2923BAIGORRIA Y VICTOR HUGOCALLEBAIGORRIANaNHUGO, VICTORBAIGORRIA y HUGO, VICTOR10Point (94810.03686085 100710.80080255)-58.51989389-34.62284918MOTO-AUTOMOTOAUTO
6902021-009212021-12-12202112122024-05-296AV. RIVADAVIA Y AV. PUEYRREDONAVENIDARIVADAVIA AV.NaNPUEYRREDON AV.RIVADAVIA AV. y PUEYRREDON AV.3Point (105258.35368554 102122.93231400)-58.40596860-34.61011987PEATON-AUTOPEATONAUTO
6912021-009312021-12-13202112132024-05-2917AV. RIESTRA Y MOMAVENIDARIESTRA AV.NaNMOMRIESTRA AV. y MOM7Point (102728.60090138 98186.24929177)-58.43353773-34.64561636MOTO-AUTOMOTOAUTO
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